
from typing import Optional, Tuple

import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE=1e-12				):
	lowercase	    =       jnp.divide(emb_a.T					,				jnp.clip(jnp.linalg.norm(__SCREAMING_SNAKE_CASE					,				axis=1				)					,				a_min=__SCREAMING_SNAKE_CASE				)				).T
	lowercase	    =       jnp.divide(emb_a.T					,				jnp.clip(jnp.linalg.norm(__SCREAMING_SNAKE_CASE					,				axis=1				)					,				a_min=__SCREAMING_SNAKE_CASE				)				).T
	return jnp.matmul(__SCREAMING_SNAKE_CASE					,				norm_emb_a.T				)







class 			A_  (     nn.Module  ):




	'''simple docstring'''


	_UpperCamelCase	:	CLIPConfig
	_UpperCamelCase	:	jnp.dtype									=			jnp.floataa





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       FlaxCLIPVisionModule(self.config.vision_config			)
		lowercase	    =       nn.Dense(self.config.projection_dim						,       use_bias=snake_case						,       dtype=self.dtype			)

		lowercase	    =       self.param('concept_embeds'						,       jax.nn.initializers.ones						,       (17, self.config.projection_dim)			)
		lowercase	    =       self.param(
		    'special_care_embeds'						,       jax.nn.initializers.ones						,       (3, self.config.projection_dim)			)

		lowercase	    =       self.param('concept_embeds_weights'						,       jax.nn.initializers.ones						,       (17,)			)
		lowercase	    =       self.param('special_care_embeds_weights'						,       jax.nn.initializers.ones						,       (3,)			)





	def __call__(			self						,       snake_case			):
		lowercase	    =       self.vision_model(snake_case			)[1]
		lowercase	    =       self.visual_projection(snake_case			)

		lowercase	    =       jax_cosine_distance(snake_case						,       self.special_care_embeds			)
		lowercase	    =       jax_cosine_distance(snake_case						,       self.concept_embeds			)

		# increase this value to create a stronger `nfsw` filter
		# at the cost of increasing the possibility of filtering benign image inputs
		lowercase	    =       0.0

		lowercase	    =       special_cos_dist - self.special_care_embeds_weights[None, :] + adjustment
		lowercase	    =       jnp.round(snake_case						,       3			)
		lowercase	    =       jnp.any(special_scores > 0						,       axis=1						,       keepdims=snake_case			)
		# Use a lower threshold if an image has any special care concept
		lowercase	    =       is_special_care * 0.01

		lowercase	    =       cos_dist - self.concept_embeds_weights[None, :] + special_adjustment
		lowercase	    =       jnp.round(snake_case						,       3			)
		lowercase	    =       jnp.any(concept_scores > 0						,       axis=1			)

		return has_nsfw_concepts







class 			A_  (     __lowerCamelCase  ):




	'''simple docstring'''


	_UpperCamelCase	:	Optional[int]									=			CLIPConfig
	_UpperCamelCase	:	Optional[int]									=			"""clip_input"""
	_UpperCamelCase	:	Optional[int]									=			FlaxStableDiffusionSafetyCheckerModule





	def __init__(			self						,       snake_case						,       snake_case = None						,       snake_case = 0						,       snake_case = jnp.floataa						,       snake_case = True						,       **snake_case						,       ):
		if input_shape is None:
			lowercase	    =       (1, 224, 224, 3)
		lowercase	    =       self.module_class(config=snake_case						,       dtype=snake_case						,       **snake_case			)
		super().__init__(snake_case						,       snake_case						,       input_shape=snake_case						,       seed=snake_case						,       dtype=snake_case						,       _do_init=_do_init			)





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case						,       snake_case = None			):
		# init input tensor
		lowercase	    =       jax.random.normal(snake_case						,       snake_case			)

		lowercase    ,   lowercase	    =       jax.random.split(snake_case			)
		lowercase	    =       {'params': params_rng, 'dropout': dropout_rng}

		lowercase	    =       self.module.init(snake_case						,       snake_case			)['params']

		return random_params





	def __call__(			self						,       snake_case						,       snake_case = None						,       ):
		lowercase	    =       jnp.transpose(snake_case						,       (0, 2, 3, 1)			)

		return self.module.apply(
		    {'params': params or self.params}						,       jnp.array(snake_case						,       dtype=jnp.floataa			)						,       rngs={}						,       )


'''simple docstring'''




import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
__UpperCAmelCase					=logging.get_logger(__name__)






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       Optional[Any]:
    __lowerCamelCase            =    UniSpeechSatForSequenceClassification.from_pretrained(UpperCamelCase__							,    config=UpperCamelCase__							)
    __lowerCamelCase            =    downstream_dict['''projector.weight''']
    __lowerCamelCase            =    downstream_dict['''projector.bias''']
    __lowerCamelCase            =    downstream_dict['''model.post_net.linear.weight''']
    __lowerCamelCase            =    downstream_dict['''model.post_net.linear.bias''']
    return model






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       Union[str, Any]:
    __lowerCamelCase            =    UniSpeechSatForAudioFrameClassification.from_pretrained(UpperCamelCase__							,    config=UpperCamelCase__							)
    __lowerCamelCase            =    downstream_dict['''model.linear.weight''']
    __lowerCamelCase            =    downstream_dict['''model.linear.bias''']
    return model






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       List[Any]:
    __lowerCamelCase            =    UniSpeechSatForXVector.from_pretrained(UpperCamelCase__							,    config=UpperCamelCase__							)
    __lowerCamelCase            =    downstream_dict['''connector.weight''']
    __lowerCamelCase            =    downstream_dict['''connector.bias''']
    for i, kernel_size in enumerate(hf_config.tdnn_kernel							):
        __lowerCamelCase            =    downstream_dict[
            f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
        ]
        __lowerCamelCase            =    downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

    __lowerCamelCase            =    downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
    __lowerCamelCase            =    downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
    __lowerCamelCase            =    downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
    __lowerCamelCase            =    downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
    __lowerCamelCase            =    downstream_dict['''objective.W''']
    return model






@torch.no_grad()
def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       Optional[Any]:
    __lowerCamelCase            =    torch.load(UpperCamelCase__							,    map_location='''cpu'''							)

    __lowerCamelCase            =    checkpoint['''Downstream''']

    __lowerCamelCase            =    UniSpeechSatConfig.from_pretrained(UpperCamelCase__							)
    __lowerCamelCase            =    WavaVecaFeatureExtractor.from_pretrained(
        UpperCamelCase__							,    return_attention_mask=UpperCamelCase__							,    do_normalize=UpperCamelCase__							)

    __lowerCamelCase            =    hf_config.architectures[0]
    if arch.endswith('''ForSequenceClassification'''							):
        __lowerCamelCase            =    convert_classification(UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)
    elif arch.endswith('''ForAudioFrameClassification'''							):
        __lowerCamelCase            =    convert_diarization(UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)
    elif arch.endswith('''ForXVector'''							):
        __lowerCamelCase            =    convert_xvector(UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)
    else:
        raise NotImplementedError(f"""S3PRL weights conversion is not supported for {arch}"""							)

    if hf_config.use_weighted_layer_sum:
        __lowerCamelCase            =    checkpoint['''Featurizer''']['''weights''']

    hf_feature_extractor.save_pretrained(UpperCamelCase__							)
    hf_model.save_pretrained(UpperCamelCase__							)


if __name__ == "__main__":
  __UpperCAmelCase					=argparse.ArgumentParser()
  parser.add_argument(
      "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
  )
  parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
  parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
  parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
  __UpperCAmelCase					=parser.parse_args()
  convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
